快速对角阵权系数协方差交叉融合容积卡尔曼滤波器

Fast covariance intersection fusion weighted by diagonal matrix cubature Kalman filter

中文关键词  非线性系统  协方差交叉融合  容积卡尔曼滤波器

英文关键词  nonlinear system  covariance intersection fusion  cubature Kalman filter

中文摘要

      针对互协方差信息未知的多传感器系统, 本文提出了一种快速对角阵权系数协方差交叉融合算法(FDCI). 本文首先提出了一种对角阵权系数协方差交叉融合(DCI)方案, 并证明了所提出DCI算法在融合估计精度上高于经 典批处理CI融合(BCI)算法. 在此基础之上, 针对非线性等复杂的互协方差未知的多传感器系统, 提出FDCI算法, 并 证明了所提出FDCI 算法的无偏性及鲁棒精度. FDCI融合算法虽然在融合估计精度上低于DCI, 但FDCI 无需进行多 权系数的非线性代价函数的优化问题, 进而大大降低了计算负担, 提高了系统的实时性. 最后, 结合容积卡尔曼滤 波算法(CKF)提出了快速对角阵权系数协方差交叉融合容积卡尔曼滤波算法. 仿真实例验证了所提出算法的正确 性和有效性.

英文摘要

      For multi-sensor systems with unknown cross-covariance, a fast covariance intersection fusion algorithm weighted by diagonal matrix (FDCI) is proposed. First, the covariance intersection fusion algorithm weighted by diagonal matrix (DCI) is proposed in this paper, and it is proved that the fusion estimation accuracy of the DCI algorithm is higher than that of the classical batch CI fusion (BCI) algorithm. Furthermore, for complex multi-sensor systems with unknown cross-covariances such as nonlinear systems, the FDCI algorithm is proposed, and its unbiasedness and robust accuracy are proved. Although the FDCI fusion algorithm has lower fusion estimation accuracy than DCI, the FDCI algorithm does not involve the optimization of nonlinear cost function with multiple weight coefficients, which greatly reduces the computational burden and improves the real-time performance of the system. Finally, combining with the cubature Kalman filter algorithm (CKF), a fast covariance intersection fusion weighted by the diagonal matrix CKF algorithm is proposed. A simulation example verifies the correctness and effectiveness of the proposed algorithm.